Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.1 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Cover_Type is highly overall correlated with Soil_Type_9 and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type_39 and 2 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Vertical_Distance_To_HydrologyHigh correlation
Soil_Type_28 is highly overall correlated with Wilderness_Area_0High correlation
Soil_Type_39 is highly overall correlated with ElevationHigh correlation
Soil_Type_9 is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Wilderness_Area_0 is highly overall correlated with Soil_Type_28 and 1 other fieldsHigh correlation
Wilderness_Area_2 is highly overall correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Wilderness_Area_1 is highly imbalanced (87.8%) Imbalance
Wilderness_Area_3 is highly imbalanced (62.2%) Imbalance
Soil_Type_0 is highly imbalanced (94.7%) Imbalance
Soil_Type_1 is highly imbalanced (92.1%) Imbalance
Soil_Type_2 is highly imbalanced (95.5%) Imbalance
Soil_Type_3 is highly imbalanced (88.7%) Imbalance
Soil_Type_4 is highly imbalanced (96.9%) Imbalance
Soil_Type_5 is highly imbalanced (90.0%) Imbalance
Soil_Type_6 is highly imbalanced (99.7%) Imbalance
Soil_Type_7 is highly imbalanced (99.5%) Imbalance
Soil_Type_8 is highly imbalanced (97.7%) Imbalance
Soil_Type_9 is highly imbalanced (70.0%) Imbalance
Soil_Type_10 is highly imbalanced (88.0%) Imbalance
Soil_Type_11 is highly imbalanced (67.5%) Imbalance
Soil_Type_12 is highly imbalanced (82.4%) Imbalance
Soil_Type_13 is highly imbalanced (99.1%) Imbalance
Soil_Type_14 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_15 is highly imbalanced (95.4%) Imbalance
Soil_Type_16 is highly imbalanced (94.5%) Imbalance
Soil_Type_17 is highly imbalanced (96.5%) Imbalance
Soil_Type_18 is highly imbalanced (94.2%) Imbalance
Soil_Type_19 is highly imbalanced (87.5%) Imbalance
Soil_Type_20 is highly imbalanced (98.2%) Imbalance
Soil_Type_21 is highly imbalanced (71.0%) Imbalance
Soil_Type_22 is highly imbalanced (54.3%) Imbalance
Soil_Type_23 is highly imbalanced (78.8%) Imbalance
Soil_Type_24 is highly imbalanced (> 99.9%) Imbalance
Soil_Type_25 is highly imbalanced (95.8%) Imbalance
Soil_Type_26 is highly imbalanced (98.8%) Imbalance
Soil_Type_27 is highly imbalanced (98.0%) Imbalance
Soil_Type_29 is highly imbalanced (67.4%) Imbalance
Soil_Type_30 is highly imbalanced (72.2%) Imbalance
Soil_Type_31 is highly imbalanced (55.7%) Imbalance
Soil_Type_32 is highly imbalanced (64.5%) Imbalance
Soil_Type_33 is highly imbalanced (98.5%) Imbalance
Soil_Type_34 is highly imbalanced (97.3%) Imbalance
Soil_Type_35 is highly imbalanced (99.7%) Imbalance
Soil_Type_36 is highly imbalanced (99.3%) Imbalance
Soil_Type_37 is highly imbalanced (82.2%) Imbalance
Soil_Type_38 is highly imbalanced (85.5%) Imbalance
Soil_Type_39 is highly imbalanced (89.7%) Imbalance
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros
Vertical_Distance_To_Hydrology has 34287 (6.8%) zeros Zeros

Reproduction

Analysis started2025-06-09 15:00:31.264640
Analysis finished2025-06-09 15:01:45.188656
Duration1 minute and 13.92 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2951.5881
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:45.262663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2381
Q12814
median2987
Q33142
95-th percentile3322
Maximum3858
Range1999
Interquartile range (IQR)328

Descriptive statistics

Standard deviation275.48424
Coefficient of variation (CV)0.093334245
Kurtosis1.0931128
Mean2951.5881
Median Absolute Deviation (MAD)163
Skewness-0.89179409
Sum1.4915968 × 109
Variance75891.569
MonotonicityNot monotonic
2025-06-09T18:01:45.353687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1625
 
0.3%
2962 1613
 
0.3%
2991 1613
 
0.3%
2972 1609
 
0.3%
2978 1603
 
0.3%
2975 1599
 
0.3%
2988 1554
 
0.3%
2955 1524
 
0.3%
2965 1519
 
0.3%
2952 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.78752
Minimum0
Maximum360
Zeros4536
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:45.439212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q157
median124
Q3263
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)206

Descriptive statistics

Standard deviation112.6629
Coefficient of variation (CV)0.72785521
Kurtosis-1.2289254
Mean154.78752
Median Absolute Deviation (MAD)84
Skewness0.41649173
Sum78222490
Variance12692.929
MonotonicityNot monotonic
2025-06-09T18:01:45.537506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 5854
 
1.2%
0 4536
 
0.9%
90 4111
 
0.8%
135 3443
 
0.7%
63 3320
 
0.7%
315 3236
 
0.6%
18 3077
 
0.6%
72 3071
 
0.6%
27 3071
 
0.6%
34 2521
 
0.5%
Other values (351) 469114
92.8%
ValueCountFrequency (%)
0 4536
0.9%
1 1506
 
0.3%
2 1709
 
0.3%
3 1718
 
0.3%
4 2023
0.4%
5 1844
0.4%
6 1981
0.4%
7 1951
0.4%
8 1983
0.4%
9 2229
0.4%
ValueCountFrequency (%)
360 49
 
< 0.1%
359 1220
0.2%
358 1566
0.3%
357 1643
0.3%
356 1801
0.4%
355 1725
0.3%
354 1784
0.4%
353 1732
0.3%
352 1782
0.4%
351 1938
0.4%

Slope
Real number (ℝ)

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767395
Minimum0
Maximum66
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:45.623022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4737982
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean13.767395
Median Absolute Deviation (MAD)5
Skewness0.85685891
Sum6957408
Variance55.857659
MonotonicityNot monotonic
2025-06-09T18:01:46.110899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 30511
 
6.0%
11 30123
 
6.0%
12 29324
 
5.8%
9 29017
 
5.7%
13 28187
 
5.6%
8 27610
 
5.5%
14 25932
 
5.1%
15 24691
 
4.9%
7 24304
 
4.8%
6 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
1 3485
 
0.7%
2 7301
 
1.4%
3 10951
 
2.2%
4 15310
3.0%
5 19417
3.8%
6 22768
4.5%
7 24304
4.8%
8 27610
5.5%
9 29017
5.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.15709
Minimum0
Maximum1397
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:46.193413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3379
95-th percentile674
Maximum1397
Range1397
Interquartile range (IQR)271

Descriptive statistics

Standard deviation211.08873
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean266.15709
Median Absolute Deviation (MAD)131
Skewness1.188776
Sum1.3450355 × 108
Variance44558.451
MonotonicityNot monotonic
2025-06-09T18:01:46.280162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 29993
 
5.9%
0 21630
 
4.3%
150 18274
 
3.6%
60 16778
 
3.3%
67 13454
 
2.7%
42 12960
 
2.6%
108 12702
 
2.5%
85 12156
 
2.4%
90 9757
 
1.9%
120 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
30 29993
5.9%
42 12960
2.6%
60 16778
3.3%
67 13454
2.7%
85 12156
2.4%
90 9757
 
1.9%
95 8120
 
1.6%
108 12702
2.5%
120 9343
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.002424
Minimum-159
Maximum601
Zeros34287
Zeros (%)6.8%
Negative45771
Negative (%)9.1%
Memory size3.9 MiB
2025-06-09T18:01:46.372408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-159
5-th percentile-7
Q17
median29
Q367
95-th percentile164
Maximum601
Range760
Interquartile range (IQR)60

Descriptive statistics

Standard deviation57.90111
Coefficient of variation (CV)1.2586535
Kurtosis5.8017203
Mean46.002424
Median Absolute Deviation (MAD)26
Skewness1.8979445
Sum23247509
Variance3352.5386
MonotonicityNot monotonic
2025-06-09T18:01:46.479796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34287
 
6.8%
3 8388
 
1.7%
10 8084
 
1.6%
7 7807
 
1.5%
13 7707
 
1.5%
6 7694
 
1.5%
4 7529
 
1.5%
5 6717
 
1.3%
16 6715
 
1.3%
23 6539
 
1.3%
Other values (682) 403887
79.9%
ValueCountFrequency (%)
-159 2
< 0.1%
-158 1
< 0.1%
-156 1
< 0.1%
-154 1
< 0.1%
-153 2
< 0.1%
-152 2
< 0.1%
-151 1
< 0.1%
-150 1
< 0.1%
-149 1
< 0.1%
-147 1
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%
Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.98
Minimum0
Maximum7117
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:46.574307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391
Q11140
median2078
Q33475
95-th percentile5571
Maximum7117
Range7117
Interquartile range (IQR)2335

Descriptive statistics

Standard deviation1600.4154
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean2431.98
Median Absolute Deviation (MAD)1085
Skewness0.65607631
Sum1.2290108 × 109
Variance2561329.4
MonotonicityNot monotonic
2025-06-09T18:01:46.669606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1078
 
0.2%
618 882
 
0.2%
900 821
 
0.2%
1020 805
 
0.2%
990 777
 
0.2%
960 764
 
0.2%
390 763
 
0.2%
1140 736
 
0.1%
1050 726
 
0.1%
750 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
30 267
0.1%
42 153
 
< 0.1%
60 280
0.1%
67 249
< 0.1%
85 293
0.1%
90 331
0.1%
95 317
0.1%
108 537
0.1%
120 559
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.28271
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:46.751720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1199
median218
Q3231
95-th percentile245
Maximum254
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.629387
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean212.28271
Median Absolute Deviation (MAD)15
Skewness-1.2439666
Sum1.0727792 × 108
Variance709.12424
MonotonicityNot monotonic
2025-06-09T18:01:46.838232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 10413
 
2.1%
228 10242
 
2.0%
224 10051
 
2.0%
230 10049
 
2.0%
223 9782
 
1.9%
222 9720
 
1.9%
233 9416
 
1.9%
227 9382
 
1.9%
225 9243
 
1.8%
221 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
56 5
 
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1641
 
0.3%
253 1822
 
0.4%
252 2123
0.4%
251 2409
0.5%
250 2766
0.5%
249 3116
0.6%
248 3249
0.6%
247 3691
0.7%
246 4069
0.8%
245 4560
0.9%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.2563
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:46.920745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.58077
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean223.2563
Median Absolute Deviation (MAD)12
Skewness-1.1404263
Sum1.1282347 × 108
Variance383.40657
MonotonicityNot monotonic
2025-06-09T18:01:47.005022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 12314
 
2.4%
228 12230
 
2.4%
233 12063
 
2.4%
230 11975
 
2.4%
229 11849
 
2.3%
234 11805
 
2.3%
227 11601
 
2.3%
226 11594
 
2.3%
223 11564
 
2.3%
225 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 3981
0.8%
253 4664
0.9%
252 5492
1.1%
251 5864
1.2%
250 6488
1.3%
249 6356
1.3%
248 6907
1.4%
247 7617
1.5%
246 7453
1.5%
245 7352
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.57913
Minimum0
Maximum254
Zeros1266
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:47.088026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1120
median143
Q3168
95-th percentile203
Maximum254
Range254
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.78155
Coefficient of variation (CV)0.26498653
Kurtosis0.54411121
Mean142.57913
Median Absolute Deviation (MAD)24
Skewness-0.29140078
Sum72052935
Variance1427.4455
MonotonicityNot monotonic
2025-06-09T18:01:47.173544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 6582
 
1.3%
145 6474
 
1.3%
138 6384
 
1.3%
146 6243
 
1.2%
142 6189
 
1.2%
136 6166
 
1.2%
139 6153
 
1.2%
149 6078
 
1.2%
135 6035
 
1.2%
150 6026
 
1.2%
Other values (245) 443024
87.7%
ValueCountFrequency (%)
0 1266
0.3%
1 14
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 18
 
< 0.1%
5 18
 
< 0.1%
6 24
 
< 0.1%
7 28
 
< 0.1%
8 20
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 10
 
< 0.1%
250 15
 
< 0.1%
249 35
< 0.1%
248 41
< 0.1%
247 54
< 0.1%
246 68
< 0.1%
245 78
< 0.1%
Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1361
Minimum0
Maximum7173
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:47.256800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11024
median1725
Q32592
95-th percentile5089
Maximum7173
Range7173
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation1357.8179
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean2010.1361
Median Absolute Deviation (MAD)771
Skewness1.266097
Sum1.0158303 × 109
Variance1843669.5
MonotonicityNot monotonic
2025-06-09T18:01:47.341317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1244
 
0.2%
541 981
 
0.2%
607 930
 
0.2%
942 856
 
0.2%
997 844
 
0.2%
700 833
 
0.2%
726 798
 
0.2%
752 782
 
0.2%
900 774
 
0.2%
960 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
30 184
< 0.1%
42 183
< 0.1%
60 182
< 0.1%
67 370
0.1%
85 183
< 0.1%
90 182
< 0.1%
95 366
0.1%
108 369
0.1%
120 180
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Wilderness_Area_0
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1.0
260796 
0.0
244558 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Length

2025-06-09T18:01:47.416828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.463695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Most occurring characters

ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Wilderness_Area_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496925 
1.0
 
8429

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Length

2025-06-09T18:01:47.520635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.559638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496925
98.3%
1.0 8429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002279
66.1%
. 505354
33.3%
1 8429
 
0.6%

Wilderness_Area_2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
306193 
1.0
199161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Length

2025-06-09T18:01:47.606161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.645161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Most occurring characters

ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Wilderness_Area_3
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
468386 
1.0
 
36968

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Length

2025-06-09T18:01:47.694396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.733175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Soil_Type_0
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502323 
1.0
 
3031

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Length

2025-06-09T18:01:47.780181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.819692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502323
99.4%
1.0 3031
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007677
66.5%
. 505354
33.3%
1 3031
 
0.2%

Soil_Type_1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
500436 
1.0
 
4918

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Length

2025-06-09T18:01:47.865692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.904209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500436
99.0%
1.0 4918
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1005790
66.3%
. 505354
33.3%
1 4918
 
0.3%

Soil_Type_2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502823 
1.0
 
2531

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Length

2025-06-09T18:01:47.951209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:47.989929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502823
99.5%
1.0 2531
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008177
66.5%
. 505354
33.3%
1 2531
 
0.2%

Soil_Type_3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497735 
1.0
 
7619

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Length

2025-06-09T18:01:48.036441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.075441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Soil_Type_4
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503757 
1.0
 
1597

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Length

2025-06-09T18:01:48.121956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.160955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503757
99.7%
1.0 1597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009111
66.6%
. 505354
33.3%
1 1597
 
0.1%

Soil_Type_5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498779 
1.0
 
6575

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Length

2025-06-09T18:01:48.208480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.249235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Soil_Type_6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505249 
1.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Length

2025-06-09T18:01:48.296757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.335758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505249
> 99.9%
1.0 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010603
66.7%
. 505354
33.3%
1 105
 
< 0.1%

Soil_Type_7
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505175 
1.0
 
179

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Length

2025-06-09T18:01:48.380766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.419283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505175
> 99.9%
1.0 179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010529
66.7%
. 505354
33.3%
1 179
 
< 0.1%

Soil_Type_8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504207 
1.0
 
1147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Length

2025-06-09T18:01:48.465284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.503220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504207
99.8%
1.0 1147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009561
66.6%
. 505354
33.3%
1 1147
 
0.1%

Soil_Type_9
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
478425 
1.0
 
26929

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Length

2025-06-09T18:01:48.556217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.595746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Soil_Type_10
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497100 
1.0
 
8254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Length

2025-06-09T18:01:48.641746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.680459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Soil_Type_11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475383 
1.0
 
29971

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Length

2025-06-09T18:01:48.728977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.766690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Soil_Type_12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
492001 
1.0
 
13353

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Length

2025-06-09T18:01:48.815215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.856215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Soil_Type_13
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504978 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Length

2025-06-09T18:01:48.902738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:48.941738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504978
99.9%
1.0 376
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010332
66.6%
. 505354
33.3%
1 376
 
< 0.1%

Soil_Type_14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505351 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Length

2025-06-09T18:01:48.989436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.027947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505351
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010705
66.7%
. 505354
33.3%
1 3
 
< 0.1%

Soil_Type_15
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502813 
1.0
 
2541

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Length

2025-06-09T18:01:49.075950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.114460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502813
99.5%
1.0 2541
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008167
66.5%
. 505354
33.3%
1 2541
 
0.2%

Soil_Type_16
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
502160 
1.0
 
3194

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Length

2025-06-09T18:01:49.162194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.201710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 502160
99.4%
1.0 3194
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007514
66.5%
. 505354
33.3%
1 3194
 
0.2%

Soil_Type_17
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503525 
1.0
 
1829

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Length

2025-06-09T18:01:49.247478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.286482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503525
99.6%
1.0 1829
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008879
66.5%
. 505354
33.3%
1 1829
 
0.1%

Soil_Type_18
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
501981 
1.0
 
3373

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Length

2025-06-09T18:01:49.335995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.373996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 501981
99.3%
1.0 3373
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1007335
66.4%
. 505354
33.3%
1 3373
 
0.2%

Soil_Type_19
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
496711 
1.0
 
8643

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Length

2025-06-09T18:01:49.420518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.460517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 496711
98.3%
1.0 8643
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002065
66.1%
. 505354
33.3%
1 8643
 
0.6%

Soil_Type_20
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504516 
1.0
 
838

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Length

2025-06-09T18:01:49.506829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.552828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504516
99.8%
1.0 838
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009870
66.6%
. 505354
33.3%
1 838
 
0.1%

Soil_Type_21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
479672 
1.0
 
25682

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Length

2025-06-09T18:01:49.599354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.636352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Soil_Type_22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
456668 
1.0
48686 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Length

2025-06-09T18:01:49.683094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.722602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Soil_Type_23
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
488395 
1.0
 
16959

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Length

2025-06-09T18:01:49.773182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.813212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 488395
96.6%
1.0 16959
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 993749
65.5%
. 505354
33.3%
1 16959
 
1.1%

Soil_Type_24
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505353 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Length

2025-06-09T18:01:49.858212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.896739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505353
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010707
66.7%
. 505354
33.3%
1 1
 
< 0.1%

Soil_Type_25
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503040 
1.0
 
2314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Length

2025-06-09T18:01:49.942739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:49.980744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503040
99.5%
1.0 2314
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1008394
66.5%
. 505354
33.3%
1 2314
 
0.2%

Soil_Type_26
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504801 
1.0
 
553

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Length

2025-06-09T18:01:50.027984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.065984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504801
99.9%
1.0 553
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010155
66.6%
. 505354
33.3%
1 553
 
< 0.1%

Soil_Type_27
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504408 
1.0
 
946

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Length

2025-06-09T18:01:50.111500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.149501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504408
99.8%
1.0 946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009762
66.6%
. 505354
33.3%
1 946
 
0.1%

Soil_Type_28
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
390180 
1.0
115174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Length

2025-06-09T18:01:50.196018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.236025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Soil_Type_29
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475184 
1.0
 
30170

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Length

2025-06-09T18:01:50.285790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.324302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Soil_Type_30
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
481101 
1.0
 
24253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Length

2025-06-09T18:01:50.371302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.408817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 481101
95.2%
1.0 24253
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 986455
65.1%
. 505354
33.3%
1 24253
 
1.6%

Soil_Type_31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
458914 
1.0
46440 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Length

2025-06-09T18:01:50.456818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.498861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Soil_Type_32
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
471457 
1.0
 
33897

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Length

2025-06-09T18:01:50.552635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.590640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 471457
93.3%
1.0 33897
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 976811
64.4%
. 505354
33.3%
1 33897
 
2.2%

Soil_Type_33
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
504648 
1.0
 
706

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Length

2025-06-09T18:01:50.636151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.674154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 504648
99.9%
1.0 706
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010002
66.6%
. 505354
33.3%
1 706
 
< 0.1%

Soil_Type_34
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
503963 
1.0
 
1391

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Length

2025-06-09T18:01:50.719391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.758136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503963
99.7%
1.0 1391
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1009317
66.6%
. 505354
33.3%
1 1391
 
0.1%

Soil_Type_35
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505235 
1.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Length

2025-06-09T18:01:50.804652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.842652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505235
> 99.9%
1.0 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010589
66.7%
. 505354
33.3%
1 119
 
< 0.1%

Soil_Type_36
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
505056 
1.0
 
298

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Length

2025-06-09T18:01:50.890657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:50.928168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 505056
99.9%
1.0 298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1010410
66.6%
. 505354
33.3%
1 298
 
< 0.1%

Soil_Type_37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
491835 
1.0
 
13519

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Length

2025-06-09T18:01:50.975168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:51.014433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Soil_Type_38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
494948 
1.0
 
10406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Length

2025-06-09T18:01:51.061433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:51.099949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Soil_Type_39
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498520 
1.0
 
6834

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Length

2025-06-09T18:01:51.148949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:01:51.186952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:01:51.220460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T18:01:51.271216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Interactions

2025-06-09T18:01:39.853964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:26.750175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.869064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.986074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.085063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.201140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.641930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.203919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.576783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.922895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.322487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.012754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:26.856333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.976321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.082606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.191572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.304133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.772412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.330194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.681654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.049407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.450820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.132051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:26.957664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.072841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.182841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.291854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.411649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.915724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.462705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.799118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.167722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.582062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.282565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.059184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.170082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.275590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.392369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.512705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.058009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.578038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.920626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.290745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.705488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.407428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.161501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.271256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.380615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.498415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.615814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.214031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.698682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.052929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.414073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.859404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.541946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.261024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.376641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.486689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.601224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.041848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.346271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.843029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.162031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.550580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.983924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.657649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.365541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.488270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.592854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.705490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.146353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.493301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.989061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.304835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.683254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:39.121526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.789681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.465605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.582382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.695243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.803583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.243353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.617031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.111062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.435348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.801278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:39.261042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:40.924946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.561724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.678727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.789754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.901089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.339370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.771419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.224577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.544601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:37.938625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:39.399795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:41.060458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.657247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.774755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.882271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:30.997405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.442891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:33.912456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.335899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.652566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.074139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:39.539308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:41.184761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:27.762543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:28.881801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:29.984550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:31.103617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:32.545810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:34.070895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:35.454415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:36.800594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:38.208973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:01:39.697451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T18:01:51.374262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectCover_TypeElevationHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysSlopeSoil_Type_0Soil_Type_1Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_2Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_3Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Vertical_Distance_To_HydrologyWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3
Aspect1.0000.0400.0320.632-0.4150.414-0.110-0.0030.0190.0680.0530.0460.0700.0990.1460.0180.0040.0220.0190.0490.0130.0460.1030.0520.0210.0340.1540.0000.0700.0660.0730.1110.1440.1540.0950.1060.0890.0320.0270.0410.0290.0450.0390.0270.0270.0250.0070.0040.0320.1800.0720.2260.1170.1980.136
Cover_Type0.0401.000-0.493-0.028-0.009-0.063-0.121-0.007-0.2260.1670.2500.2930.1070.2050.1370.1640.0140.0300.1950.0630.0450.0450.4170.0520.2010.1970.0700.0000.0510.0220.0380.2060.1470.2580.0820.1020.0870.0340.1810.0420.1280.3630.3340.2220.1810.3150.0140.0100.0340.5140.1210.3450.0760.1620.817
Elevation0.032-0.4931.0000.0730.0260.1860.1290.2610.417-0.1750.3650.2250.1720.2860.1360.1300.0160.0760.1360.2310.0630.0880.2670.0260.2160.1770.0870.0010.0690.0530.0660.2340.1220.1890.0920.1870.0870.0500.1300.0470.0780.4090.3390.6280.2890.3420.0200.0270.1820.5470.0600.2880.1450.2220.926
Hillshade_3pm0.632-0.0280.0731.000-0.8190.566-0.0830.0350.105-0.1870.1590.0210.0750.1440.2230.0060.0000.0360.0370.0520.0580.0670.1650.0500.0440.1410.0300.0070.0330.0790.1820.1000.1480.0330.0580.1320.1100.0170.0260.0130.0260.0640.0950.0450.0650.0200.0120.0170.0280.1530.0360.1990.1020.1260.208
Hillshade_9am-0.415-0.0090.026-0.8191.000-0.0860.126-0.0390.007-0.1250.0520.0350.0450.1240.1390.0100.0000.0250.0240.0370.0560.0560.0880.0340.0490.1240.1230.0000.0380.0570.1590.0910.1510.0440.0880.1050.0970.0120.0260.0090.0120.0470.0580.0190.0760.0280.0120.0180.0270.307-0.1320.2310.0810.1340.278
Hillshade_Noon0.414-0.0630.1860.566-0.0861.0000.0200.0320.209-0.4680.0940.0250.0740.0850.0690.0110.0010.0160.0300.0230.0480.0370.0480.0340.0390.1320.1350.0000.0300.0240.0080.0840.0460.0700.0550.1260.1170.0230.0080.0170.0260.0470.0590.0460.0840.0170.0070.0150.0140.235-0.0990.1030.0470.0640.236
Horizontal_Distance_To_Fire_Points-0.110-0.1210.129-0.0830.1260.0201.0000.0740.371-0.1690.1240.0880.0760.2990.1160.0450.0040.1100.0430.1710.0270.1290.0780.0330.0810.0900.0900.0060.0800.0400.0420.2220.0740.0680.0830.1330.0910.0360.0290.0230.0380.0870.0560.0760.0700.1160.0880.0450.0560.217-0.0390.4070.1300.2830.340
Horizontal_Distance_To_Hydrology-0.003-0.0070.2610.035-0.0390.0320.0741.0000.0720.0130.0370.0470.0420.0810.0200.0390.0000.0750.0930.0190.0500.1000.0310.0510.0590.1870.0450.0000.0250.1630.0370.0790.0550.0380.0690.1430.0670.1630.0430.0700.0190.0700.0420.1870.0170.0430.0210.0110.0270.0670.6240.1130.0250.1650.107
Horizontal_Distance_To_Roadways0.019-0.2260.4170.1050.0070.2090.3710.0721.000-0.2280.1460.1030.1250.1040.1450.0330.0050.0570.0650.0800.1050.0870.0850.0410.1500.0680.0770.0000.0920.0440.0770.3210.1160.1030.1610.1830.1340.0440.0490.0350.0500.1090.1110.0910.1100.1550.0550.0480.0680.238-0.0320.4830.1680.4220.398
Slope0.0680.167-0.175-0.187-0.125-0.468-0.1690.013-0.2281.0000.1350.0190.0650.1760.2050.0040.0000.0340.0410.0460.1030.0760.1170.0260.0640.2060.1120.0040.0450.0550.0980.0900.1000.1070.0860.1410.2250.0150.0170.0170.0110.0690.0930.0420.0940.0150.0210.0360.0340.2650.3160.2160.0720.1270.311
Soil_Type_00.0530.2500.3650.1590.0520.0940.1240.0370.1460.1351.0000.0070.0100.0190.0130.0010.0000.0050.0060.0040.0060.0100.0050.0020.0180.0250.0140.0000.0050.0020.0030.0420.0190.0090.0170.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0180.0260.0800.0100.0630.276
Soil_Type_10.0460.2930.2250.0210.0350.0250.0880.0470.1030.0190.0071.0000.0130.0250.0160.0020.0000.0070.0080.0060.0080.0130.0070.0040.0230.0320.0180.0000.0060.0030.0040.0540.0250.0120.0220.0310.0270.0030.0050.0000.0010.0160.0140.0110.0050.0110.0000.0000.0040.0230.0420.1020.0130.0340.138
Soil_Type_100.0700.1070.1720.0750.0450.0740.0760.0420.1250.0650.0100.0131.0000.0320.0210.0030.0000.0090.0100.0080.0100.0170.0090.0050.0300.0420.0240.0000.0090.0040.0050.0700.0320.0160.0290.0410.0340.0040.0060.0000.0020.0210.0190.0150.0070.0150.0000.0010.0060.0310.0220.1330.0170.1410.000
Soil_Type_110.0990.2050.2860.1440.1240.0850.2990.0810.1040.1760.0190.0250.0321.0000.0410.0070.0000.0180.0200.0150.0200.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1360.0630.0310.0560.0800.0670.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0740.2430.0330.2020.071
Soil_Type_120.1460.1370.1360.2230.1390.0690.1160.0200.1450.2050.0130.0160.0210.0411.0000.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0890.0410.0200.0370.0520.0440.0060.0080.0020.0030.0270.0240.0190.0090.0190.0010.0020.0080.0390.1060.1700.0190.2040.046
Soil_Type_130.0180.1640.1300.0060.0100.0110.0450.0390.0330.0040.0010.0020.0030.0070.0041.0000.0000.0000.0010.0000.0010.0030.0000.0000.0060.0090.0050.0000.0000.0000.0000.0150.0070.0030.0060.0080.0070.0000.0000.0000.0000.0040.0030.0030.0000.0020.0000.0000.0000.0060.0150.0280.0030.0190.092
Soil_Type_140.0040.0140.0160.0000.0000.0010.0040.0000.0050.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.007
Soil_Type_150.0220.0300.0760.0360.0250.0160.1100.0750.0570.0340.0050.0070.0090.0180.0120.0000.0001.0000.0050.0040.0050.0090.0050.0020.0160.0230.0130.0000.0040.0010.0020.0390.0180.0090.0160.0230.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0420.0460.0070.0500.008
Soil_Type_160.0190.1950.1360.0370.0240.0300.0430.0930.0650.0410.0060.0080.0100.0200.0130.0010.0000.0051.0000.0040.0060.0100.0050.0030.0180.0260.0150.0000.0050.0020.0030.0430.0200.0100.0180.0250.0210.0020.0040.0000.0000.0130.0110.0090.0040.0090.0000.0000.0030.0190.0440.0820.0100.0580.054
Soil_Type_170.0490.0630.2310.0520.0370.0230.1710.0190.0800.0460.0040.0060.0080.0150.0100.0000.0000.0040.0041.0000.0050.0080.0040.0010.0140.0200.0110.0000.0040.0010.0020.0330.0150.0070.0130.0190.0160.0010.0020.0000.0000.0100.0090.0070.0030.0070.0000.0000.0020.0140.0350.0580.0080.0490.017
Soil_Type_180.0130.0450.0630.0580.0560.0480.0270.0500.1050.1030.0060.0080.0100.0200.0130.0010.0000.0050.0060.0051.0000.0110.0050.0030.0190.0270.0150.0000.0050.0020.0030.0440.0210.0100.0180.0260.0220.0020.0040.0000.0000.0130.0120.0090.0040.0090.0000.0000.0030.0190.0420.0490.0130.0410.023
Soil_Type_190.0460.0450.0880.0670.0560.0370.1290.1000.0870.0760.0100.0130.0170.0330.0220.0030.0000.0090.0100.0080.0111.0000.0090.0050.0300.0430.0240.0000.0090.0040.0050.0720.0330.0160.0300.0420.0350.0050.0070.0010.0030.0220.0190.0150.0070.0150.0000.0020.0060.0310.0610.0700.0170.0470.037
Soil_Type_20.1030.4170.2670.1650.0880.0480.0780.0310.0850.1170.0050.0070.0090.0180.0120.0000.0000.0050.0050.0040.0050.0091.0000.0020.0160.0230.0130.0000.0040.0010.0020.0380.0180.0090.0160.0220.0190.0020.0030.0000.0000.0120.0100.0080.0030.0080.0000.0000.0030.0170.0330.0730.0090.0530.244
Soil_Type_200.0520.0520.0260.0500.0340.0340.0330.0510.0410.0260.0020.0040.0050.0100.0060.0000.0000.0020.0030.0010.0030.0050.0021.0000.0090.0130.0070.0000.0020.0000.0000.0220.0100.0050.0090.0130.0110.0000.0010.0000.0000.0060.0060.0040.0010.0040.0000.0000.0000.0090.0240.0420.0050.0500.011
Soil_Type_210.0210.2010.2160.0440.0490.0390.0810.0590.1500.0640.0180.0230.0300.0580.0380.0060.0000.0160.0180.0140.0190.0300.0160.0091.0000.0760.0430.0000.0160.0070.0100.1260.0580.0290.0520.0740.0620.0080.0120.0030.0050.0380.0330.0270.0130.0260.0030.0040.0110.0550.0830.1150.1040.1110.065
Soil_Type_220.0340.1970.1770.1410.1240.1320.0900.1870.0680.2060.0250.0320.0420.0820.0540.0090.0000.0230.0260.0200.0270.0430.0230.0130.0761.0000.0610.0000.0220.0110.0140.1770.0820.0400.0730.1040.0880.0120.0170.0050.0080.0540.0470.0380.0180.0370.0040.0060.0150.0770.1530.0460.0950.0230.092
Soil_Type_230.1540.0700.0870.0300.1230.1350.0900.0450.0770.1120.0140.0180.0240.0470.0310.0050.0000.0130.0150.0110.0150.0240.0130.0070.0430.0611.0000.0000.0120.0060.0080.1010.0470.0230.0420.0590.0500.0070.0100.0020.0040.0310.0270.0220.0100.0210.0020.0030.0090.0440.0440.1290.0870.1370.052
Soil_Type_240.0000.0000.0010.0070.0000.0000.0060.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0050.0000.000
Soil_Type_250.0700.0510.0690.0330.0380.0300.0800.0250.0920.0450.0050.0060.0090.0170.0110.0000.0000.0040.0050.0040.0050.0090.0040.0020.0160.0220.0120.0001.0000.0010.0020.0370.0170.0080.0150.0210.0180.0020.0030.0000.0000.0110.0100.0080.0030.0080.0000.0000.0030.0160.0190.0700.0090.0840.019
Soil_Type_260.0660.0220.0530.0790.0570.0240.0400.1630.0440.0550.0020.0030.0040.0080.0050.0000.0000.0010.0020.0010.0020.0040.0010.0000.0070.0110.0060.0000.0011.0000.0000.0180.0080.0040.0070.0100.0090.0000.0000.0000.0000.0050.0040.0030.0000.0030.0000.0000.0000.0080.1780.0340.0040.0410.009
Soil_Type_270.0730.0380.0660.1820.1590.0080.0420.0370.0770.0980.0030.0040.0050.0110.0070.0000.0000.0020.0030.0020.0030.0050.0020.0000.0100.0140.0080.0000.0020.0001.0000.0230.0110.0050.0100.0140.0110.0000.0010.0000.0000.0070.0060.0050.0010.0050.0000.0000.0010.0100.1250.0450.0050.0540.012
Soil_Type_280.1110.2060.2340.1000.0910.0840.2220.0790.3210.0900.0420.0540.0700.1360.0890.0150.0000.0390.0430.0330.0440.0720.0380.0220.1260.1770.1010.0000.0370.0180.0231.0000.1370.0670.1220.1730.1460.0200.0280.0080.0130.0900.0790.0640.0310.0620.0080.0100.0260.1290.0960.5260.0710.4380.153
Soil_Type_290.1440.1470.1220.1480.1510.0460.0740.0550.1160.1000.0190.0250.0320.0630.0410.0070.0000.0180.0200.0150.0210.0330.0180.0100.0580.0820.0470.0000.0170.0080.0110.1371.0000.0310.0570.0800.0680.0090.0130.0030.0060.0420.0360.0290.0140.0290.0030.0040.0120.0600.0270.2440.0330.2030.071
Soil_Type_30.1540.2580.1890.0330.0440.0700.0680.0380.1030.1070.0090.0120.0160.0310.0200.0030.0000.0090.0100.0070.0100.0160.0090.0050.0290.0400.0230.0000.0080.0040.0050.0670.0311.0000.0280.0390.0330.0040.0060.0000.0020.0200.0180.0140.0070.0140.0000.0010.0060.0290.0250.1280.0160.1120.042
Soil_Type_300.0950.0820.0920.0580.0880.0550.0830.0690.1610.0860.0170.0220.0290.0560.0370.0060.0000.0160.0180.0130.0180.0300.0160.0090.0520.0730.0420.0000.0150.0070.0100.1220.0570.0281.0000.0710.0600.0080.0120.0030.0050.0370.0320.0260.0120.0260.0030.0040.0110.0530.0410.2320.0290.2780.063
Soil_Type_310.1060.1020.1870.1320.1050.1260.1330.1430.1830.1410.0250.0310.0410.0800.0520.0080.0000.0230.0250.0190.0260.0420.0220.0130.0740.1040.0590.0000.0210.0100.0140.1730.0800.0390.0711.0000.0850.0120.0170.0040.0070.0530.0460.0370.0180.0360.0040.0060.0150.0750.0490.3280.0410.3730.089
Soil_Type_320.0890.0870.0870.1100.0970.1170.0910.0670.1340.2250.0210.0270.0340.0670.0440.0070.0000.0190.0210.0160.0220.0350.0190.0110.0620.0880.0500.0000.0180.0090.0110.1460.0680.0330.0600.0851.0000.0100.0140.0040.0060.0440.0390.0310.0150.0310.0030.0050.0130.0640.1450.2770.0170.3280.075
Soil_Type_330.0320.0340.0500.0170.0120.0230.0360.1630.0440.0150.0020.0030.0040.0090.0060.0000.0000.0020.0020.0010.0020.0050.0020.0000.0080.0120.0070.0000.0020.0000.0000.0200.0090.0040.0080.0120.0101.0000.0000.0000.0000.0060.0050.0040.0010.0040.0000.0000.0000.0090.1030.0390.0040.0460.010
Soil_Type_340.0270.1810.1300.0260.0260.0080.0290.0430.0490.0170.0040.0050.0060.0130.0080.0000.0000.0030.0040.0020.0040.0070.0030.0010.0120.0170.0100.0000.0030.0000.0010.0280.0130.0060.0120.0170.0140.0001.0000.0000.0000.0080.0070.0060.0020.0060.0000.0000.0020.0120.0280.0040.0050.0140.015
Soil_Type_350.0410.0420.0470.0130.0090.0170.0230.0700.0350.0170.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0010.0000.0000.0030.0050.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0040.0000.0001.0000.0000.0020.0010.0000.0000.0000.0000.0000.0000.0030.0250.0160.0010.0190.004
Soil_Type_360.0290.1280.0780.0260.0120.0260.0380.0190.0500.0110.0000.0010.0020.0060.0030.0000.0000.0000.0000.0000.0000.0030.0000.0000.0050.0080.0040.0000.0000.0000.0000.0130.0060.0020.0050.0070.0060.0000.0000.0001.0000.0040.0030.0020.0000.0020.0000.0000.0000.0050.0060.0130.0020.0080.007
Soil_Type_370.0450.3630.4090.0640.0470.0470.0870.0700.1090.0690.0130.0160.0210.0420.0270.0040.0000.0120.0130.0100.0130.0220.0120.0060.0380.0540.0310.0000.0110.0050.0070.0900.0420.0200.0370.0530.0440.0060.0080.0020.0041.0000.0240.0190.0090.0190.0010.0020.0080.0390.0240.0130.0140.0150.047
Soil_Type_380.0390.3340.3390.0950.0580.0590.0560.0420.1110.0930.0110.0140.0190.0360.0240.0030.0000.0100.0110.0090.0120.0190.0100.0060.0330.0470.0270.0000.0100.0040.0060.0790.0360.0180.0320.0460.0390.0050.0070.0010.0030.0241.0000.0170.0080.0170.0010.0020.0070.0340.0550.0390.0150.0140.041
Soil_Type_390.0270.2220.6280.0450.0190.0460.0760.1870.0910.0420.0090.0110.0150.0290.0190.0030.0000.0080.0090.0070.0090.0150.0080.0040.0270.0380.0220.0000.0080.0030.0050.0640.0290.0140.0260.0370.0310.0040.0060.0000.0020.0190.0171.0000.0060.0130.0000.0010.0050.0280.2410.0280.0490.0240.033
Soil_Type_40.0270.1810.2890.0650.0760.0840.0700.0170.1100.0940.0040.0050.0070.0140.0090.0000.0000.0030.0040.0030.0040.0070.0030.0010.0130.0180.0100.0000.0030.0000.0010.0310.0140.0070.0120.0180.0150.0010.0020.0000.0000.0090.0080.0061.0000.0060.0000.0000.0020.0130.0360.0580.0070.0450.200
Soil_Type_50.0250.3150.3420.0200.0280.0170.1160.0430.1550.0150.0090.0110.0150.0290.0190.0020.0000.0080.0090.0070.0090.0150.0080.0040.0260.0370.0210.0000.0080.0030.0050.0620.0290.0140.0260.0360.0310.0040.0060.0000.0020.0190.0170.0130.0061.0000.0000.0010.0050.0270.0550.1190.0150.0930.409
Soil_Type_60.0070.0140.0200.0120.0120.0070.0880.0210.0550.0210.0000.0000.0000.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0080.0030.0000.0030.0040.0030.0000.0000.0000.0000.0010.0010.0000.0000.0001.0000.0000.0000.0030.0080.0140.0000.0110.004
Soil_Type_70.0040.0100.0270.0170.0180.0150.0450.0110.0480.0360.0000.0000.0010.0040.0020.0000.0000.0000.0000.0000.0000.0020.0000.0000.0040.0060.0030.0000.0000.0000.0000.0100.0040.0010.0040.0060.0050.0000.0000.0000.0000.0020.0020.0010.0000.0010.0001.0000.0000.0040.0110.0180.0010.0150.005
Soil_Type_80.0320.0340.1820.0280.0270.0140.0560.0270.0680.0340.0030.0040.0060.0120.0080.0000.0000.0030.0030.0020.0030.0060.0030.0000.0110.0150.0090.0000.0030.0000.0010.0260.0120.0060.0110.0150.0130.0000.0020.0000.0000.0080.0070.0050.0020.0050.0000.0001.0000.0110.0310.0460.0060.0380.013
Soil_Type_90.1800.5140.5470.1530.3070.2350.2170.0670.2380.2650.0180.0230.0310.0600.0390.0060.0000.0170.0190.0140.0190.0310.0170.0090.0550.0770.0440.0000.0160.0080.0100.1290.0600.0290.0530.0750.0640.0090.0120.0030.0050.0390.0340.0280.0130.0270.0030.0040.0111.0000.0900.2450.0310.0290.539
Vertical_Distance_To_Hydrology0.0720.1210.0600.036-0.132-0.099-0.0390.624-0.0320.3160.0260.0420.0220.0740.1060.0150.0000.0420.0440.0350.0420.0610.0330.0240.0830.1530.0440.0050.0190.1780.1250.0960.0270.0250.0410.0490.1450.1030.0280.0250.0060.0240.0550.2410.0360.0550.0080.0110.0310.0901.0000.1940.0680.1660.106
Wilderness_Area_00.2260.3450.2880.1990.2310.1030.4070.1130.4830.2160.0800.1020.1330.2430.1700.0280.0010.0460.0820.0580.0490.0700.0730.0420.1150.0460.1290.0000.0700.0340.0450.5260.2440.1280.2320.3280.2770.0390.0040.0160.0130.0130.0390.0280.0580.1190.0140.0180.0460.2450.1941.0000.1340.8330.290
Wilderness_Area_10.1170.0760.1450.1020.0810.0470.1300.0250.1680.0720.0100.0130.0170.0330.0190.0030.0000.0070.0100.0080.0130.0170.0090.0050.1040.0950.0870.0050.0090.0040.0050.0710.0330.0160.0290.0410.0170.0040.0050.0010.0020.0140.0150.0490.0070.0150.0000.0010.0060.0310.0680.1341.0000.1050.037
Wilderness_Area_20.1980.1620.2220.1260.1340.0640.2830.1650.4220.1270.0630.0340.1410.2020.2040.0190.0000.0500.0580.0490.0410.0470.0530.0500.1110.0230.1370.0000.0840.0410.0540.4380.2030.1120.2780.3730.3280.0460.0140.0190.0080.0150.0140.0240.0450.0930.0110.0150.0380.0290.1660.8330.1051.0000.227
Wilderness_Area_30.1360.8170.9260.2080.2780.2360.3400.1070.3980.3110.2760.1380.0000.0710.0460.0920.0070.0080.0540.0170.0230.0370.2440.0110.0650.0920.0520.0000.0190.0090.0120.1530.0710.0420.0630.0890.0750.0100.0150.0040.0070.0470.0410.0330.2000.4090.0040.0050.0130.5390.1060.2900.0370.2271.000

Missing values

2025-06-09T18:01:41.445867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T18:01:42.770709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
02596.051.03.0258.00.0510.0221.0232.0148.06279.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
12590.056.02.0212.0-6.0390.0220.0235.0151.06225.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
22804.0139.09.0268.065.03180.0234.0238.0135.06121.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
32785.0155.018.0242.0118.03090.0238.0238.0122.06211.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.02
42595.045.02.0153.0-1.0391.0220.0234.0150.06172.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
52579.0132.06.0300.0-15.067.0230.0237.0140.06031.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.02
62606.045.07.0270.05.0633.0222.0225.0138.06256.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
72605.049.04.0234.07.0573.0222.0230.0144.06228.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
82617.045.09.0240.056.0666.0223.0221.0133.06244.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
92612.059.010.0247.011.0636.0228.0219.0124.06230.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.05
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_1Wilderness_Area_2Wilderness_Area_3Soil_Type_0Soil_Type_1Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_15Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Cover_Type
5053443190.056.012.0190.014.01597.0228.0214.0117.01584.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053453183.060.016.0162.07.01595.0231.0205.0102.01608.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053463175.053.017.0134.020.01593.0227.0203.0104.01632.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053473169.044.015.0108.014.01591.0222.0205.0114.01657.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053483164.048.014.085.09.01590.0224.0209.0116.01681.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053493158.061.013.060.013.01590.0230.0211.0111.01706.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053503151.068.013.030.06.01590.0233.0214.0111.01731.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01
5053513145.056.013.00.00.01591.0228.0213.0116.01756.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01
5053523140.041.016.00.00.01593.0221.0204.0114.01781.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01
5053533134.029.017.030.01.01595.0211.0200.0120.01806.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01